Overview

Dataset statistics

Number of variables21
Number of observations145
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory176.0 B

Variable types

Categorical3
Text1
Unsupported4
Numeric13

Alerts

class has constant value "Diabetic"Constant
flag has constant value "real"Constant
file_path has unique valuesUnique
rms_acc_x has unique valuesUnique
rms_acc_y has unique valuesUnique
path has unique valuesUnique
samp_en_x has unique valuesUnique
samp_en_y has unique valuesUnique
f80_x has unique valuesUnique
f80_y has unique valuesUnique
mf_lf_x has unique valuesUnique
mf_lf_y has unique valuesUnique
mf_mf_x has unique valuesUnique
mf_mf_y has unique valuesUnique
mf_hf_x has unique valuesUnique
mf_hf_y has unique valuesUnique
cop_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
cop_y is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_y is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-22 08:30:59.826348
Analysis finished2024-05-22 08:31:15.792072
Duration15.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

class
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Diabetic
145 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1160
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiabetic
2nd rowDiabetic
3rd rowDiabetic
4th rowDiabetic
5th rowDiabetic

Common Values

ValueCountFrequency (%)
Diabetic 145
100.0%

Length

2024-05-22T04:31:15.873092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:31:15.968120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
diabetic 145
100.0%

Most occurring characters

ValueCountFrequency (%)
i 290
25.0%
D 145
12.5%
a 145
12.5%
b 145
12.5%
e 145
12.5%
t 145
12.5%
c 145
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 290
25.0%
D 145
12.5%
a 145
12.5%
b 145
12.5%
e 145
12.5%
t 145
12.5%
c 145
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 290
25.0%
D 145
12.5%
a 145
12.5%
b 145
12.5%
e 145
12.5%
t 145
12.5%
c 145
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 290
25.0%
D 145
12.5%
a 145
12.5%
b 145
12.5%
e 145
12.5%
t 145
12.5%
c 145
12.5%

file_path
Text

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:16.124154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60
Median length59
Mean length57.855172
Min length56

Characters and Unicode

Total characters8389
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)100.0%

Sample

1st row../data/raw/COP_Tests/DG\participant_1\EBOA 1_SWARII.txt
2nd row../data/raw/COP_Tests/DG\participant_1\EBOA 2_SWARII.txt
3rd row../data/raw/COP_Tests/DG\participant_1\EBOA 3_SWARII.txt
4th row../data/raw/COP_Tests/DG\participant_1\EBOASI1_SWARII.txt
5th row../data/raw/COP_Tests/DG\participant_1\EBOASI2_SWARII.txt
ValueCountFrequency (%)
2_swarii.txt 47
 
16.7%
1_swarii.txt 47
 
16.7%
3_swarii.txt 42
 
14.9%
data/raw/cop_tests/dg\participant_1\eboa 3
 
1.1%
data/raw/cop_tests/dg\participant_2\eboa 3
 
1.1%
data/raw/cop_tests/dg\participant_7\eboasi 3
 
1.1%
data/raw/cop_tests/dg\participant_7\eboa 3
 
1.1%
data/raw/cop_tests/dg\participant_2\eboasi 3
 
1.1%
data/raw/cop_tests/dg\participant_2\eboasitd 3
 
1.1%
data/raw/cop_tests/dg\participant_2\eboatd 3
 
1.1%
Other values (49) 124
44.1%
2024-05-22T04:31:16.456409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 870
 
10.4%
a 725
 
8.6%
/ 580
 
6.9%
. 435
 
5.2%
_ 435
 
5.2%
I 358
 
4.3%
p 290
 
3.5%
\ 290
 
3.5%
s 290
 
3.5%
i 290
 
3.5%
Other values (27) 3826
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 870
 
10.4%
a 725
 
8.6%
/ 580
 
6.9%
. 435
 
5.2%
_ 435
 
5.2%
I 358
 
4.3%
p 290
 
3.5%
\ 290
 
3.5%
s 290
 
3.5%
i 290
 
3.5%
Other values (27) 3826
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 870
 
10.4%
a 725
 
8.6%
/ 580
 
6.9%
. 435
 
5.2%
_ 435
 
5.2%
I 358
 
4.3%
p 290
 
3.5%
\ 290
 
3.5%
s 290
 
3.5%
i 290
 
3.5%
Other values (27) 3826
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 870
 
10.4%
a 725
 
8.6%
/ 580
 
6.9%
. 435
 
5.2%
_ 435
 
5.2%
I 358
 
4.3%
p 290
 
3.5%
\ 290
 
3.5%
s 290
 
3.5%
i 290
 
3.5%
Other values (27) 3826
45.6%

exam
Categorical

Distinct10
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
EBOA
21 
EBOC
19 
EBOCTD
19 
EBOATD
18 
EBOASI
17 
Other values (5)
51 

Length

Max length8
Median length6
Mean length5.9172414
Min length4

Characters and Unicode

Total characters858
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEBOA
2nd rowEBOA
3rd rowEBOA
4th rowEBOASI
5th rowEBOASI

Common Values

ValueCountFrequency (%)
EBOA 21
14.5%
EBOC 19
13.1%
EBOCTD 19
13.1%
EBOATD 18
12.4%
EBOASI 17
11.7%
EBOCSI 17
11.7%
EBOASITD 11
7.6%
EBOCSITD 11
7.6%
EBOATDSI 6
 
4.1%
EBOCTDSI 6
 
4.1%

Length

2024-05-22T04:31:16.638698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:31:16.764725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
eboa 21
14.5%
eboc 19
13.1%
eboctd 19
13.1%
eboatd 18
12.4%
eboasi 17
11.7%
ebocsi 17
11.7%
eboasitd 11
7.6%
ebocsitd 11
7.6%
eboatdsi 6
 
4.1%
eboctdsi 6
 
4.1%

Most occurring characters

ValueCountFrequency (%)
E 145
16.9%
B 145
16.9%
O 145
16.9%
A 73
8.5%
C 72
8.4%
T 71
8.3%
D 71
8.3%
S 68
7.9%
I 68
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 145
16.9%
B 145
16.9%
O 145
16.9%
A 73
8.5%
C 72
8.4%
T 71
8.3%
D 71
8.3%
S 68
7.9%
I 68
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 145
16.9%
B 145
16.9%
O 145
16.9%
A 73
8.5%
C 72
8.4%
T 71
8.3%
D 71
8.3%
S 68
7.9%
I 68
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 145
16.9%
B 145
16.9%
O 145
16.9%
A 73
8.5%
C 72
8.4%
T 71
8.3%
D 71
8.3%
S 68
7.9%
I 68
7.9%

cop_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.3 KiB

cop_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.3 KiB

flag
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
real
145 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters580
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreal
2nd rowreal
3rd rowreal
4th rowreal
5th rowreal

Common Values

ValueCountFrequency (%)
real 145
100.0%

Length

2024-05-22T04:31:16.924763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:31:17.024782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
real 145
100.0%

Most occurring characters

ValueCountFrequency (%)
r 145
25.0%
e 145
25.0%
a 145
25.0%
l 145
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 145
25.0%
e 145
25.0%
a 145
25.0%
l 145
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 145
25.0%
e 145
25.0%
a 145
25.0%
l 145
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 145
25.0%
e 145
25.0%
a 145
25.0%
l 145
25.0%

acc_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.3 KiB

acc_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.3 KiB

rms_acc_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.258517
Minimum0.25914433
Maximum3.6350462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:17.143961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.25914433
5-th percentile0.33439459
Q10.58997181
median1.0024691
Q31.614508
95-th percentile3.2672617
Maximum3.6350462
Range3.3759019
Interquartile range (IQR)1.0245362

Descriptive statistics

Standard deviation0.87666155
Coefficient of variation (CV)0.69658298
Kurtosis0.54624676
Mean1.258517
Median Absolute Deviation (MAD)0.44978577
Skewness1.1837494
Sum182.48497
Variance0.76853547
MonotonicityNot monotonic
2024-05-22T04:31:17.283994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3260067847 1
 
0.7%
0.3022313856 1
 
0.7%
2.143757605 1
 
0.7%
1.403567964 1
 
0.7%
1.918805703 1
 
0.7%
1.512191204 1
 
0.7%
1.4110637 1
 
0.7%
0.832120853 1
 
0.7%
0.8302815091 1
 
0.7%
2.951127077 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.2591443278 1
0.7%
0.3022313856 1
0.7%
0.308388578 1
0.7%
0.3124144887 1
0.7%
0.3160563604 1
0.7%
0.3226532419 1
0.7%
0.3260067847 1
0.7%
0.3339899268 1
0.7%
0.3360132492 1
0.7%
0.3383619281 1
0.7%
ValueCountFrequency (%)
3.635046221 1
0.7%
3.521455577 1
0.7%
3.379886078 1
0.7%
3.371286647 1
0.7%
3.35143677 1
0.7%
3.320655425 1
0.7%
3.304007668 1
0.7%
3.270471469 1
0.7%
3.254422552 1
0.7%
3.250178474 1
0.7%

rms_acc_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6744599
Minimum0.58563669
Maximum4.7358037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:17.416021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.58563669
5-th percentile0.71496771
Q11.1772546
median1.5574802
Q32.064029
95-th percentile2.8947581
Maximum4.7358037
Range4.150167
Interquartile range (IQR)0.88677442

Descriptive statistics

Standard deviation0.72213868
Coefficient of variation (CV)0.43126663
Kurtosis1.8511159
Mean1.6744599
Median Absolute Deviation (MAD)0.45189432
Skewness1.0736062
Sum242.79669
Variance0.52148427
MonotonicityNot monotonic
2024-05-22T04:31:17.545050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.234905424 1
 
0.7%
0.6531737766 1
 
0.7%
2.300909793 1
 
0.7%
1.49952771 1
 
0.7%
2.043581626 1
 
0.7%
1.656620217 1
 
0.7%
1.353137291 1
 
0.7%
1.757837831 1
 
0.7%
1.950597266 1
 
0.7%
2.906900619 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.5856366856 1
0.7%
0.615411436 1
0.7%
0.6302159496 1
0.7%
0.6521667451 1
0.7%
0.6531737766 1
0.7%
0.6699356414 1
0.7%
0.6762216743 1
0.7%
0.7141642556 1
0.7%
0.7181815059 1
0.7%
0.7548183754 1
0.7%
ValueCountFrequency (%)
4.735803706 1
0.7%
3.763980729 1
0.7%
3.5972047 1
0.7%
3.534036663 1
0.7%
3.510281648 1
0.7%
3.083738004 1
0.7%
2.931239731 1
0.7%
2.906900619 1
0.7%
2.846187973 1
0.7%
2.749589159 1
0.7%

path
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.06076
Minimum80.653179
Maximum1008.5273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:17.667091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum80.653179
5-th percentile144.6703
Q1230.39979
median285.76605
Q3408.71683
95-th percentile644.31575
Maximum1008.5273
Range927.87409
Interquartile range (IQR)178.31704

Descriptive statistics

Standard deviation161.0815
Coefficient of variation (CV)0.48656173
Kurtosis2.5875359
Mean331.06076
Median Absolute Deviation (MAD)73.90362
Skewness1.4449397
Sum48003.81
Variance25947.249
MonotonicityNot monotonic
2024-05-22T04:31:17.797114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296.434712 1
 
0.7%
183.9238584 1
 
0.7%
361.1533936 1
 
0.7%
281.9303543 1
 
0.7%
326.6206521 1
 
0.7%
223.3378774 1
 
0.7%
340.8983002 1
 
0.7%
295.5794942 1
 
0.7%
316.0320277 1
 
0.7%
522.6824591 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
80.65317932 1
0.7%
101.1873642 1
0.7%
115.5370644 1
0.7%
121.3219372 1
0.7%
127.9170362 1
0.7%
139.0355561 1
0.7%
139.63642 1
0.7%
144.1811905 1
0.7%
146.6267406 1
0.7%
150.7161662 1
0.7%
ValueCountFrequency (%)
1008.527271 1
0.7%
888.4785434 1
0.7%
801.0020137 1
0.7%
789.4488021 1
0.7%
660.157469 1
0.7%
658.9624648 1
0.7%
655.2655496 1
0.7%
645.1747631 1
0.7%
640.8796774 1
0.7%
628.4483967 1
0.7%

samp_en_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15629083
Minimum0.0061142936
Maximum0.5049259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:17.926375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0061142936
5-th percentile0.026325307
Q10.061370803
median0.11366843
Q30.21502756
95-th percentile0.41706552
Maximum0.5049259
Range0.49881161
Interquartile range (IQR)0.15365676

Descriptive statistics

Standard deviation0.12387867
Coefficient of variation (CV)0.79261635
Kurtosis0.19712706
Mean0.15629083
Median Absolute Deviation (MAD)0.068913192
Skewness1.0737568
Sum22.66217
Variance0.015345924
MonotonicityNot monotonic
2024-05-22T04:31:18.078397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0262144319 1
 
0.7%
0.0252377168 1
 
0.7%
0.3186173035 1
 
0.7%
0.1970319096 1
 
0.7%
0.2761956821 1
 
0.7%
0.2031745821 1
 
0.7%
0.1706180369 1
 
0.7%
0.0991553243 1
 
0.7%
0.0927052113 1
 
0.7%
0.3987009958 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.0061142936 1
0.7%
0.0179495302 1
0.7%
0.0194367352 1
0.7%
0.0221201324 1
0.7%
0.0226633118 1
0.7%
0.0234826439 1
0.7%
0.0252377168 1
0.7%
0.0262144319 1
0.7%
0.026768806 1
0.7%
0.026918962 1
0.7%
ValueCountFrequency (%)
0.5049259016 1
0.7%
0.4794228512 1
0.7%
0.4577484674 1
0.7%
0.4474252699 1
0.7%
0.426893108 1
0.7%
0.4245110705 1
0.7%
0.4235924927 1
0.7%
0.4170950598 1
0.7%
0.4169473451 1
0.7%
0.414663368 1
0.7%

samp_en_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21827868
Minimum0.050509683
Maximum0.52520618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:18.220435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.050509683
5-th percentile0.079512048
Q10.14211294
median0.20797001
Q30.28853043
95-th percentile0.39175229
Maximum0.52520618
Range0.47469649
Interquartile range (IQR)0.14641749

Descriptive statistics

Standard deviation0.10035018
Coefficient of variation (CV)0.45973421
Kurtosis-0.25637874
Mean0.21827868
Median Absolute Deviation (MAD)0.073160492
Skewness0.5442248
Sum31.650408
Variance0.010070158
MonotonicityNot monotonic
2024-05-22T04:31:18.351458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1640801225 1
 
0.7%
0.0633395022 1
 
0.7%
0.3433432475 1
 
0.7%
0.2129036343 1
 
0.7%
0.2785150921 1
 
0.7%
0.2139160305 1
 
0.7%
0.1904972061 1
 
0.7%
0.2399547157 1
 
0.7%
0.2499533658 1
 
0.7%
0.3636817654 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.0505096829 1
0.7%
0.0633395022 1
0.7%
0.0640366595 1
0.7%
0.0648011554 1
0.7%
0.0654811802 1
0.7%
0.0732397568 1
0.7%
0.0767464542 1
0.7%
0.0789925748 1
0.7%
0.081589939 1
0.7%
0.0828299134 1
0.7%
ValueCountFrequency (%)
0.5252061779 1
0.7%
0.4699560996 1
0.7%
0.4544238035 1
0.7%
0.4295388524 1
0.7%
0.4167680244 1
0.7%
0.4058541375 1
0.7%
0.4032752412 1
0.7%
0.3923617592 1
0.7%
0.3893143916 1
0.7%
0.3743585719 1
0.7%

f80_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.87544
Minimum83.723193
Maximum1752.3511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:18.480486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum83.723193
5-th percentile159.13714
Q1318.96869
median509.56544
Q3771.35312
95-th percentile1379.5523
Maximum1752.3511
Range1668.6279
Interquartile range (IQR)452.38443

Descriptive statistics

Standard deviation369.47281
Coefficient of variation (CV)0.62635733
Kurtosis0.87176132
Mean589.87544
Median Absolute Deviation (MAD)216.78309
Skewness1.1365207
Sum85531.939
Variance136510.16
MonotonicityNot monotonic
2024-05-22T04:31:18.616530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
223.5560484 1
 
0.7%
291.9557627 1
 
0.7%
812.1815812 1
 
0.7%
601.2477794 1
 
0.7%
726.3485312 1
 
0.7%
590.1815958 1
 
0.7%
705.3583992 1
 
0.7%
361.3902653 1
 
0.7%
427.6134274 1
 
0.7%
1170.743891 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
83.72319299 1
0.7%
117.4162025 1
0.7%
122.3874193 1
0.7%
130.5949988 1
0.7%
134.4794073 1
0.7%
134.5016009 1
0.7%
156.2953379 1
0.7%
157.0978714 1
0.7%
167.294225 1
0.7%
168.9048713 1
0.7%
ValueCountFrequency (%)
1752.351102 1
0.7%
1717.555541 1
0.7%
1617.785999 1
0.7%
1523.612717 1
0.7%
1444.065125 1
0.7%
1432.706348 1
0.7%
1416.779285 1
0.7%
1381.492262 1
0.7%
1371.792486 1
0.7%
1359.370467 1
0.7%

f80_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean726.9731
Minimum241.43573
Maximum1744.9533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:18.749546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum241.43573
5-th percentile376.10713
Q1532.55872
median670.67664
Q3864.23272
95-th percentile1261.1804
Maximum1744.9533
Range1503.5175
Interquartile range (IQR)331.67401

Descriptive statistics

Standard deviation277.39373
Coefficient of variation (CV)0.38157358
Kurtosis1.1361381
Mean726.9731
Median Absolute Deviation (MAD)148.4152
Skewness1.0149811
Sum105411.1
Variance76947.282
MonotonicityNot monotonic
2024-05-22T04:31:18.883576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701.5266459 1
 
0.7%
428.3704007 1
 
0.7%
821.5207362 1
 
0.7%
607.1560714 1
 
0.7%
736.0650005 1
 
0.7%
535.9529928 1
 
0.7%
653.8929434 1
 
0.7%
800.1950622 1
 
0.7%
804.2285832 1
 
0.7%
1174.503417 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
241.4357278 1
0.7%
303.0666067 1
0.7%
327.6482601 1
0.7%
332.8057896 1
0.7%
347.0314389 1
0.7%
354.7246486 1
0.7%
355.6780477 1
0.7%
375.0468173 1
0.7%
380.3483746 1
0.7%
383.5648493 1
0.7%
ValueCountFrequency (%)
1744.953277 1
0.7%
1641.967344 1
0.7%
1399.561577 1
0.7%
1376.404053 1
0.7%
1361.954908 1
0.7%
1279.712907 1
0.7%
1267.205493 1
0.7%
1264.174082 1
0.7%
1249.205649 1
0.7%
1239.60141 1
0.7%

mf_lf_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.236896
Minimum3.6342512
Maximum117.76468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:19.019611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.6342512
5-th percentile9.9362652
Q121.322886
median30.520631
Q347.825304
95-th percentile86.190237
Maximum117.76468
Range114.13043
Interquartile range (IQR)26.502418

Descriptive statistics

Standard deviation22.928756
Coefficient of variation (CV)0.63274614
Kurtosis1.8432506
Mean36.236896
Median Absolute Deviation (MAD)11.452244
Skewness1.3320027
Sum5254.35
Variance525.72786
MonotonicityNot monotonic
2024-05-22T04:31:19.163639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.38472303 1
 
0.7%
20.41714826 1
 
0.7%
46.05100071 1
 
0.7%
33.94449706 1
 
0.7%
40.79140127 1
 
0.7%
30.15493004 1
 
0.7%
45.33456658 1
 
0.7%
19.32378674 1
 
0.7%
32.54418244 1
 
0.7%
62.60641846 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
3.634251197 1
0.7%
4.431744437 1
0.7%
5.831378094 1
0.7%
6.181009446 1
0.7%
6.867540994 1
0.7%
7.356403657 1
0.7%
8.261740093 1
0.7%
9.875356509 1
0.7%
10.17989974 1
0.7%
10.92225976 1
0.7%
ValueCountFrequency (%)
117.7646793 1
0.7%
112.3437315 1
0.7%
111.2006831 1
0.7%
98.10147079 1
0.7%
91.215756 1
0.7%
91.05944588 1
0.7%
88.49806983 1
0.7%
87.67998448 1
0.7%
80.23124761 1
0.7%
78.66144229 1
0.7%

mf_lf_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.180816
Minimum12.156987
Maximum108.68488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:19.300027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.156987
5-th percentile22.765435
Q131.654782
median39.342765
Q351.140988
95-th percentile71.437764
Maximum108.68488
Range96.52789
Interquartile range (IQR)19.486205

Descriptive statistics

Standard deviation16.843315
Coefficient of variation (CV)0.39006476
Kurtosis1.5623897
Mean43.180816
Median Absolute Deviation (MAD)8.9935503
Skewness1.1342368
Sum6261.2183
Variance283.69725
MonotonicityNot monotonic
2024-05-22T04:31:19.445073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.84788071 1
 
0.7%
33.43252269 1
 
0.7%
41.67656465 1
 
0.7%
34.23539609 1
 
0.7%
38.60101698 1
 
0.7%
22.54086614 1
 
0.7%
47.34502995 1
 
0.7%
47.63420292 1
 
0.7%
44.92550274 1
 
0.7%
55.94794113 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
12.15698716 1
0.7%
17.67015785 1
0.7%
18.78953081 1
0.7%
18.90563336 1
0.7%
19.40502117 1
0.7%
20.70863248 1
0.7%
22.54086614 1
0.7%
22.70522389 1
0.7%
23.00627983 1
0.7%
23.3072816 1
0.7%
ValueCountFrequency (%)
108.6848776 1
0.7%
91.38436369 1
0.7%
89.76573728 1
0.7%
88.61940861 1
0.7%
88.53762085 1
0.7%
83.69307918 1
0.7%
74.50891863 1
0.7%
71.49242178 1
0.7%
71.21913038 1
0.7%
71.0527291 1
0.7%

mf_mf_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.439876
Minimum1.9313668
Maximum31.874234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:19.566087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.9313668
5-th percentile2.7383056
Q15.1235903
median8.2652167
Q313.693583
95-th percentile27.962575
Maximum31.874234
Range29.942867
Interquartile range (IQR)8.5699927

Descriptive statistics

Standard deviation7.3409639
Coefficient of variation (CV)0.70316584
Kurtosis0.91710243
Mean10.439876
Median Absolute Deviation (MAD)4.0713918
Skewness1.2605892
Sum1513.782
Variance53.889751
MonotonicityNot monotonic
2024-05-22T04:31:19.693115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.237556337 1
 
0.7%
3.854262611 1
 
0.7%
15.22821561 1
 
0.7%
10.90474541 1
 
0.7%
13.75326581 1
 
0.7%
11.2670206 1
 
0.7%
11.71516543 1
 
0.7%
7.037524508 1
 
0.7%
6.002638593 1
 
0.7%
24.1090963 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
1.931366821 1
0.7%
2.434748685 1
0.7%
2.469324388 1
0.7%
2.512686198 1
0.7%
2.680094916 1
0.7%
2.688205941 1
0.7%
2.704793511 1
0.7%
2.711574862 1
0.7%
2.845228783 1
0.7%
2.872204862 1
0.7%
ValueCountFrequency (%)
31.87423413 1
0.7%
30.73480086 1
0.7%
30.66055678 1
0.7%
29.64530825 1
0.7%
28.87792456 1
0.7%
28.29918643 1
0.7%
28.04165571 1
0.7%
27.97008174 1
0.7%
27.9325497 1
0.7%
26.2762933 1
0.7%

mf_mf_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.193294
Minimum4.0750593
Maximum36.517657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:19.815157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.0750593
5-th percentile5.1136944
Q19.2428469
median12.217626
Q316.102702
95-th percentile23.341327
Maximum36.517657
Range32.442598
Interquartile range (IQR)6.8598549

Descriptive statistics

Standard deviation5.8835836
Coefficient of variation (CV)0.44595259
Kurtosis1.8203357
Mean13.193294
Median Absolute Deviation (MAD)3.4103996
Skewness1.1082129
Sum1913.0276
Variance34.616556
MonotonicityNot monotonic
2024-05-22T04:31:19.963237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.77995748 1
 
0.7%
4.874393431 1
 
0.7%
16.31564848 1
 
0.7%
11.05601772 1
 
0.7%
14.92952599 1
 
0.7%
12.21762602 1
 
0.7%
9.801284364 1
 
0.7%
15.25339597 1
 
0.7%
15.41963989 1
 
0.7%
25.15277789 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
4.075059311 1
0.7%
4.282397621 1
0.7%
4.46464023 1
0.7%
4.650767794 1
0.7%
4.874393431 1
0.7%
4.974624839 1
0.7%
5.005485224 1
0.7%
5.082620525 1
0.7%
5.237990065 1
0.7%
5.327051773 1
0.7%
ValueCountFrequency (%)
36.51765691 1
0.7%
33.41180088 1
0.7%
30.08244621 1
0.7%
27.48938666 1
0.7%
26.26211497 1
0.7%
25.15277789 1
0.7%
24.1947849 1
0.7%
23.5300867 1
0.7%
22.58628618 1
0.7%
22.57753284 1
0.7%

mf_hf_x
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5444768
Minimum0.17668142
Maximum7.0030736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:20.412325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.17668142
5-th percentile0.27357255
Q10.72123817
median1.2273703
Q32.2057569
95-th percentile3.5772712
Maximum7.0030736
Range6.8263922
Interquartile range (IQR)1.4845187

Descriptive statistics

Standard deviation1.1529995
Coefficient of variation (CV)0.74653079
Kurtosis4.1593951
Mean1.5444768
Median Absolute Deviation (MAD)0.67711873
Skewness1.6300185
Sum223.94913
Variance1.3294078
MonotonicityNot monotonic
2024-05-22T04:31:20.577362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9617785005 1
 
0.7%
1.128633561 1
 
0.7%
2.446762524 1
 
0.7%
2.124309786 1
 
0.7%
2.185591924 1
 
0.7%
2.454176911 1
 
0.7%
1.922434255 1
 
0.7%
1.184355723 1
 
0.7%
0.7248950505 1
 
0.7%
2.852319763 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.1766814157 1
0.7%
0.1952573484 1
0.7%
0.2105730476 1
0.7%
0.2342067324 1
0.7%
0.2456733461 1
0.7%
0.2567159151 1
0.7%
0.2592618662 1
0.7%
0.2707023985 1
0.7%
0.2850531645 1
0.7%
0.2938685216 1
0.7%
ValueCountFrequency (%)
7.003073584 1
0.7%
5.934264494 1
0.7%
5.435877436 1
0.7%
3.986460256 1
0.7%
3.854484156 1
0.7%
3.799052627 1
0.7%
3.777716623 1
0.7%
3.616946199 1
0.7%
3.418571068 1
0.7%
3.205946931 1
0.7%

mf_hf_y
Real number (ℝ)

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0277348
Minimum0.55500536
Maximum5.7940852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-22T04:31:20.724409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.55500536
5-th percentile0.85331133
Q11.2919244
median1.788841
Q32.4836178
95-th percentile3.9743548
Maximum5.7940852
Range5.2390798
Interquartile range (IQR)1.1916934

Descriptive statistics

Standard deviation0.99547753
Coefficient of variation (CV)0.49093082
Kurtosis1.5768567
Mean2.0277348
Median Absolute Deviation (MAD)0.61240888
Skewness1.1632756
Sum294.02155
Variance0.9909755
MonotonicityNot monotonic
2024-05-22T04:31:20.863425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.125769396 1
 
0.7%
1.372649275 1
 
0.7%
3.016645485 1
 
0.7%
2.122764917 1
 
0.7%
2.154632527 1
 
0.7%
1.950094974 1
 
0.7%
1.246933721 1
 
0.7%
1.657497988 1
 
0.7%
2.336037624 1
 
0.7%
3.851663074 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
0.5550053648 1
0.7%
0.6885160268 1
0.7%
0.6936543106 1
0.7%
0.7307459784 1
0.7%
0.7692602292 1
0.7%
0.8110721611 1
0.7%
0.8315182677 1
0.7%
0.8525903346 1
0.7%
0.8561952914 1
0.7%
0.8918891528 1
0.7%
ValueCountFrequency (%)
5.794085161 1
0.7%
5.434986447 1
0.7%
4.986695064 1
0.7%
4.231657907 1
0.7%
4.113072852 1
0.7%
4.00481946 1
0.7%
4.001895916 1
0.7%
3.996872808 1
0.7%
3.88428254 1
0.7%
3.880851687 1
0.7%

Interactions

2024-05-22T04:31:14.092661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:00.083408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:01.289294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:02.547877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:03.843729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:04.968085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:06.027393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:07.148235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:08.288049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:09.672278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:10.865498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T04:31:12.905858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:31:13.993619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

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A simple visualization of nullity by column.
2024-05-22T04:31:15.663042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

classfile_pathexamcop_xcop_yflagacc_xacc_yrms_acc_xrms_acc_ypathsamp_en_xsamp_en_yf80_xf80_ymf_lf_xmf_lf_ymf_mf_xmf_mf_ymf_hf_xmf_hf_y
87Diabetic../data/raw/COP_Tests/DG\participant_1\EBOA 1_SWARII.txtEBOA[-0.35748559010000003, -0.3461731127, -0.33069883840000003, -0.31392210060000003, -0.3018743003, -0.2932834989, -0.2779642955, -0.2662224519, -0.2614735163, -0.2470482542, -0.23370853830000002, -0.21007495280000002, -0.1966106233, -0.18141757930000002, -0.1664073123, -0.15843681110000002, -0.15405909010000002, -0.153787062, -0.15597444300000002, -0.15628207630000002, -0.1619216497, -0.1640810968, -0.15992246670000002, -0.1620794753, -0.1629027348, -0.1645865374, -0.1660786829, -0.1737323688, -0.18755235720000002, -0.2064519794, -0.2316748405, -0.2650158664, -0.2973318871, -0.3306800828, -0.3578113217, -0.3745231049, -0.3827376145, -0.3777601266, -0.3701148204, -0.35327211080000004, -0.3299604103, -0.3069053251, -0.2881047155, -0.2747862195, -0.2584204943, -0.2413479382, -0.2250813613, -0.2076661851, -0.1924345093, -0.17205446300000002, -0.1575613505, -0.1458474459, -0.144469234, -0.1520392372, -0.1632617728, 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